# 1644 search results for "tutorial"

## What is a Bayes factor?

February 9, 2014
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The BayesFactor packageThis blog is a companion to the BayesFactor package in R (website), which supports inference by Bayes factors in common research designs. Bayes factors have been proposed as more principled replacements for common classical statistical procedures such as \(p\) values; this blog will offer tutorials in using the package for data analysis.In this first post, I...

## Use SQL to operate R data frames

February 6, 2014
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In both research and application, we need to manipulate data frames by selecting desired columns, filtering records, transforming and aggregating data. R provides built-in functions for data frame manipulation. Suppose df is the data frame we are dealing with. We use df to select the first 100 rows, df to select price and volume columns, df to...

## In case you missed it: January 2014 roundup

February 5, 2014
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In case you missed them, here are some articles from January of particular interest to R users: Princeton’s Germán Rodríguez has published a useful “Introduction to R” guide, with a focus on linear and logistic regression. The rxDForest function in the RevoScaleR package fits random forests of histogram-binning trees. A tutorial on using the xts package to analyze and...

## “Show me the way to the next whiskey bar” (The Doors – Alabama Song) – Interactive Location Recommendation using Tableau

February 2, 2014
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Since I started using Tableau I’m quite fascinated about the capabilities of this piece of software. Before Christmas I was looking how I could build an interactive visualization that helps me to explore the relationships between different objects in a form that shows which objects are very close to each other according to some similarity measure or vice versa....

## An idiot learns Bayesian analysis: Part 2

February 1, 2014
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A week ago, I wrote a bit about my personal journey to come to grips with Bayesian inference. I referred to the epiphany that when we're talking about Bayesian analysis, what we're talking about- in a tangible way- is using and modifying multivariate distributions. This reminds me of the moment, about twenty years ago now,

## Introducing the ecoengine package

January 30, 2014
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Natural history museums have long been valuable repositories of data on species diversity. These data have been critical for fostering and shaping the development of fields such as biogeography and systematics. The importance of these data repositories is becoming increasingly important, especially in the context of climate change, where a strong understanding of how species responded to past...

## Ryan Peek on Creating Shiny Apps

January 28, 2014
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Yesterday at the Davis R User’s Group1, Ryan Peek gave a talk about using the shiny package to create interactive web apps with R. Here are his slides. Ryan includes a bunch of links to examples and tutorials, as well as his own thermohydrographs app: Thanks to Revolution Analytics for another year of...

## R: Essentials

January 25, 2014
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It is quite easy to get started with R. The very first step is to download R from the official website , and install it. I suggest that you install both 32-bit and 64-bit versions for greater compatibility if you are running a 64-bit operating system. For typical statistical programming, if your dataset is not huge, it...

## R: Getting Started

January 23, 2014
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R rocks in both academia and industry nowadays. A rapidly increasing number of researchers choose R to be one of their productive tools for data analysis and data visualization. It is partially because the software is totally free and open-source but also because the community behind the stage who contributes to nearly 5000 packages remains growing, which results in...

## A brief foray into parallel processing with R

January 21, 2014
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I’ve recently been dabbling with parallel processing in R and have found the foreach package to be a useful approach to increasing efficiency of loops. To date, I haven’t had much of a need for these tools but I’ve started working with large datasets that can be cumbersome to manage. My first introduction to parallel